A Combination of Transfer Learning and Support Vector Machine for Robust Classification on Small Weed and Potato Datasets

Faisal Adhinata - Institut Teknologi Telkom Purwokerto, Indonesia
Nur Ramadhan - Institut Teknologi Telkom Purwokerto, Indonesia
Nia Tanjung - Institut Teknologi Telkom Purwokerto, Indonesia
Muhammad Fauzi - Institut Teknologi Telkom Surabaya, Indonesia
Nia Annisa Tanjung - Institut Teknologi Telkom Purwokerto, Indonesia

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DOI: http://dx.doi.org/10.30630/joiv.7.2.1164


Agriculture is the primary sector in Indonesia for meeting people's daily food demands. One of the agricultural commodities that replace rice is potatoes. Potato growth needs to be protected from weeds that compete for nutrients. Spraying using pesticides can cause environmental pollution, affecting cultivated plants. Currently, agricultural technology is being developed using an Artificial Intelligence (AI) approach to classifying crops. The classification process using AI depends on the number of datasets obtained. The number of datasets obtained in this research is not too large, so it requires a particular approach regarding the AI method used. This research aims to use a combination of feature extraction methods with local and deep feature approaches with supervised machine learning to classify of small datasets. The local feature method used in this research is Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG), while the deep feature method used is MobileNet and MobileNetV2. The famous Support Vector Machine (SVM) uses the classification method to separate two data classes. The experimental results showed that the local feature HOG method was the fastest in the training process. However, the most accurate result was using the MobileNetV2 deep feature method with an accuracy of 98%. Deep features produced the best accuracy because the feature extraction process went through many neural network layers. This research can provide insight on how to analyze a small number of datasets by combining several strategies


Weed; potatoes; small dataset; machine learning; local and deep feature

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